Home Courses Facebook Friend Recommendation using Graph Mining Exercise: Perform EDA on Haberman dataset

Exercise: Perform EDA on Haberman dataset

Instructor: Applied AI Course Duration: 4 mins

CLick here to download IPYTHON notes for this chapter EDA

Assignment:

Data Visualization with Haberman Dataset

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This is the first assignments on data visualization.

You can get the data from https://www.kaggle.com/gilsousa/habermans-survival-data-set.

try to document every plot and every analysis you do, please refer attached notebook. Note: Upload both python notebook and pdf version of that notebook Check this: https://ipython.org/ipython-doc/3/notebook/nbconvert.html to convert python notebooks to pdfs.

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Python for Data Science Introduction

1.1

Why learn Python?

4 min

1.2

Keywords and identifiers

6 min

1.3

comments, indentation and statements

9 min

1.4

Variables and data types in Python

32 min

1.5

Standard Input and Output

7 min

1.6

14 min

1.7

Control flow: if else

10 min

1.8

Control flow: while loop

16 min

1.9

Control flow: for loop

15 min

1.10

Control flow: break and continue

10 min

1.11

Python, Anaconda and relevant packages installations

23 min

Plotting for exploratory data analysis (EDA)

2.1

Introduction to IRIS dataset and 2D scatter plot

26 min

2.2

3D scatter plot

6 min

2.3

14 min

2.4

Limitations of pair plots

2 min

2.5

Histogram and Introduction to PDF(Probability Density Function)

17 min

2.6

Univariate Analysis using PDF

6 min

2.7

CDF(Cumulative Distribution Function)

15 min

2.8

Mean, Variance and Standard Deviation

17 min

2.9

10 min

2.10

Percentiles and Quantiles

9 min

2.11

IQR(Inter Quartile Range) and MAD(Median Absolute Deviation)

6 min

2.12

Box-plot with Whiskers

9 min

2.13

4 min

2.14

Summarizing Plots, Univariate, Bivariate and Multivariate analysis

6 min

2.15

Multivariate Probability Density, Contour Plot

9 min

2.16

Exercise: Perform EDA on Haberman dataset

4 min

Linear Algebra

3.1

4 min

3.2

Introduction to Vectors(2-D, 3-D, n-D) , Row Vector and Column Vector

14 min

3.3

Distance of a point from a Plane/Hyperplane, Half-Spaces

10 min

3.4

Dot Product and Angle between 2 Vectors -

14 min

3.5

Projection and Unit Vector

5 min

3.6

Equation of a line (2-D), Plane(3-D) and Hyperplane (n-D), Plane Passing through origin, Normal to a Plane

23 min

3.7

Equation of a Circle (2-D), Sphere (3-D) and Hypersphere (n-D)

7 min

3.8

Equation of an Ellipse (2-D), Ellipsoid (3-D) and Hyperellipsoid (n-D)

6 min

3.9

Hyper Cube,Hyper Cuboid

3 min

3.10

Assignment[Optional]

30 min

Interview Questions on Linear Algebra

4.1

Questions & Answers

30 min

Probability and Statistics

5.1

Gaussian/Normal Distribution and its PDF(Probability Density Function)

27 min

5.2

CDF(Cumulative Distribution function) of Gaussian/Normal distribution

11 min

5.3

14 min

Interview Questions on Probability and statistics

6.1

Questions & Answers

30 min

Dimensionality reduction and Visualization:

7.1

what is dimensionality reduction?

3 min

7.2

Row Vector and Column Vector

5 min

7.3

How to represent a data set?

4 min

7.4

How to represent a dataset as a Matrix.

7 min

7.5

Data preprocessing: Column Normalization

20 min

7.6

Mean of a data matrix

6 min

7.7

Data preprocessing: Column Standardization

16 min

7.8

Co-variance of a Data Matrix

24 min

7.9

MNIST dataset (784 dimensional)

20 min

7.10

Code to load MNIST Dataset

12 min

Interview Questions on Dimensionality Reduction

8.1

Questions & Answers

30 min

PCA(principal component analysis)

9.1

4 min

9.2

Geometric intuition of PCA

14 min

9.3

Mathematical objective function of PCA

13 min

9.4

Alternative formulation of PCA: Distance minimization

10 min

9.5

Eigen values and Eigen vectors

23 min

9.6

PCA for Dimensionality Reduction and Visualization

10 min

9.7

Visualize MNIST dataset

5 min

9.8

Limitations of PCA

5 min

9.9

PCA Code example using Visualization

19 min

9.10

PCA Code example using non-Visualization

15 min

(t-SNE)T-distributed Stochastic Neighbourhood Embedding

10.1

7 min

10.2

Neighborhood of a point, Embedding

7 min

10.3

Geometric intuition of t-SNE

9 min

10.4

Crowding Problem

8 min

10.5

How to apply t-SNE and interpret its output

38 min

10.6

7 min

10.7

Code example of t-SNE

9 min

Interview Questions on K-NN(K Nearest Neighbour)

11.1

Questions & Answers

30 min

Interview Questions on Classification algorithms in various situations

12.1

Questions & Answers

30 min

Interview Questions on Performance Measurement Models

13.1

Questions & Answers

30 min

Interview Questions on Naive Bayes Algorithm

14.1

Questions & Answers

30 min

Solving optimization problems

15.1

Interview Questions on Logistic Regression and Linear Regression

30 min

Interview Questions on Logistic Regression and Linear Regression

16.1

Questions & Answers

30 min

Interview Questions on Support Vector Machine

17.1

Questions & Answers

30 min

Interview Questions on decision Trees

18.1

Questions & Answers

30 min

Interview Questions on Ensemble Models

19.1

Questions & Answers

30 min